This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose

The purpose of the study was to develop an objective measure of meditation quality
based on the machine classification of multiple simultaneous physiological recordings.

Methods

We recorded 32-channel EEG, electro-oculogram (EOG), ECG, respiration, and movement
from 12 experienced (mean=13.3 years of practice) Zen Buddhist meditators and 12 non-meditator
controls under two conditions for each group: listening to an audiotape and either
loving-kindness meditation (meditators) or sitting quietly (non-meditators). The data
were split into 15 minute audiotape and meditation epochs. Data were further divided
into training and classification sets, and the support vector machine (SVM)-light
algorithm was trained on data from each subject.

Results

Performance of the SVM classifier is measured as the mean AUC for the receiver operating
characteristic on the classification set. Perfect separation is AUC =1.0, whereas
chance classification is AUC = 0.5. The best performing feature set across subjects
was the respiration signal, AUC = 0.90. The EEG (based on the 7 common artifact-free
channels) and EOG classification performance had mean AUC values of 0.85 and 0.77,
respectively. Frequency domain features analyzed included alpha band (mean AUC 0.54)
and scalp EMG (mean AUC of 0.68).

Conclusion

The classifier was able to reliably separate meditating and non-meditating states
using the physiological measures. We were also able to construct a preliminary performance
hierarchy of response variables: respiration, EEG, EOG, EMG, and alpha power. The
probability of classification can be interpreted as a measure of meditation ability
by using the trained classifier to predict class membership in novice meditators.
In summary, we have demonstrated a proof-of-concept objective measure of meditation
quality.